Sports Training Strategies and Interactive Control Methods Based on Neural Network Models

Area of Science:

• Sports Science
• Artificial Intelligence
• Human-Computer Interaction

Background:

• Traditional sports training lacks scientific integration with modern technology.
• The 5G Internet of Everything era enables new data collection and analysis methods.
• Developing effective and scientific sports training strategies is crucial for athlete performance.

Purpose of the Study:

• To formulate advanced sports training strategies by integrating science and technology.
• To enhance sports coaching through data-driven insights and human-computer interaction.
• To guide scientific training methodologies for improved athletic outcomes.

Main Methods:

• Utilizing wireless sensors for comprehensive data collection.
• Applying deep learning algorithms within neural networks for data analysis.
• Developing an interactive control strategy for sports training systems.

Main Results:

• Achieved deep learning of sensor data through neural networks.
• Enabled effective human-computer interaction for training guidance.
• Demonstrated a 20% improvement in training efficiency via interactive exercise training.

Conclusions:

• The integration of technology, particularly sensors and neural networks, significantly advances sports training.
• Interactive control strategies enhance the scientific basis and effectiveness of athletic preparation.
• This approach offers a more personalized and efficient training experience for athletes.